• Collaborate with business stakeholders to understand complex business scenarios and translate them into actionable data models and metrics.

• Develop and implement data quality checks and monitoring processes to ensure data accuracy and integrity.

• Architect and build data infrastructure, including data lakes, data warehouses, and data marts, leveraging open-source solutions where appropriate.

• Design and develop reports, dashboards, and visualizations to provide insights into business performance using tools like Superset, Tableau.

• Proactively identify and resolve data-related issues, including data anomalies, performance bottlenecks, and data quality problems.

• Drive data governance initiatives, including data documentation, data lineage, and data access control.

• Participate in data architecture discussions and contribute to the development of data strategy and roadmap.

• Plan and manage data infrastructure projects, including resource allocation, timelines, and deliverables.

• Stay up-to-date with the latest data management technologies and best practices, particularly in the open-source ecosystem.

• Evaluate and recommend new technologies and tools to improve the efficiency and effectiveness of the data team.

• University Degree (or above) in Computer Science, Software Engineering, or a related discipline.

• 10+ years of experience in data engineering or data management roles, with a strong focus on data warehousing and data lake solutions.

• Proven experience in leading and mentoring data engineering teams.

• Expertise in designing and implementing complex data models, including dimensional modelling and star schema.

• Strong SQL skills and proficiency in at least one programme language (e.g., Python, Java).

• Experience with data warehouse technologies such as Doris, ClickHouse, Snowflake, AWS Redshift, or Google BigQuery.

• Experience with data processing frameworks such as Apache Flink.

• Experience with workflow management tools like Apache Airflow.

• Experience with data visualization tools such as Superset or similar open-source solutions.

• Strong understanding of data quality principles and best practices.

• Excellent communication and collaboration skills, with the ability to effectively interact with both technical and non-technical stakeholders.